在未知性能函数下，如何高效分配有限评估预算。
Budget Allocation for Unknown Value Functions in a Lipschitz Space
- 基于利普希茨空间建模未知函数，指导预算分配
- 在有限评估次数内逼近最优模型，提升探索效率
- 适合超参调优、特征选择等需要频繁评估的场景
构建学习模型常需评估大量中间模型，如特征选择、结构搜索和参数调优过程中。中间模型的评估结果影响后续探索决策。尽管先验知识可提供初步质量估计，但真实性能只有在评估后才能确定。本文研究在有限预算下，如何最优地探索中间模型空间。将问题形式化为利普希茨空间中的未知价值函数预算分配问题，旨在以最少评估次数逼近最优模型。
原文摘要 · Abstract (English)
Building learning models frequently requires evaluating numerous intermediate models. Examples include models considered during feature selection, model structure search, and parameter tunings. The evaluation of an intermediate model influences subsequent model exploration decisions. Although prior knowledge can provide initial quality estimates, true performance is only revealed after evaluation. In this work, we address the challenge of optimally allocating a bounded budget to explore the space of intermediate models. We formalize this as a general budget allocation problem over unknown-value functions within a Lipschitz space.
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